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    "# Assignment 2\n",
    "\n",
    "Before working on this assignment please read these instructions fully. In the submission area, you will notice that you can click the link to **Preview the Grading** for each step of the assignment. This is the criteria that will be used for peer grading. Please familiarize yourself with the criteria before beginning the assignment.\n",
    "\n",
    "An NOAA dataset has been stored in the file `data/C2A2_data/BinnedCsvs_d400/fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89.csv`. This is the dataset to use for this assignment. Note: The data for this assignment comes from a subset of The National Centers for Environmental Information (NCEI) [Daily Global Historical Climatology Network](https://www1.ncdc.noaa.gov/pub/data/ghcn/daily/readme.txt) (GHCN-Daily). The GHCN-Daily is comprised of daily climate records from thousands of land surface stations across the globe.\n",
    "\n",
    "Each row in the assignment datafile corresponds to a single observation.\n",
    "\n",
    "The following variables are provided to you:\n",
    "\n",
    "* **id** : station identification code\n",
    "* **date** : date in YYYY-MM-DD format (e.g. 2012-01-24 = January 24, 2012)\n",
    "* **element** : indicator of element type\n",
    "    * TMAX : Maximum temperature (tenths of degrees C)\n",
    "    * TMIN : Minimum temperature (tenths of degrees C)\n",
    "* **value** : data value for element (tenths of degrees C)\n",
    "\n",
    "For this assignment, you must:\n",
    "\n",
    "1. Read the documentation and familiarize yourself with the dataset, then write some python code which returns a line graph of the record high and record low temperatures by day of the year over the period 2005-2014. The area between the record high and record low temperatures for each day should be shaded.\n",
    "2. Overlay a scatter of the 2015 data for any points (highs and lows) for which the ten year record (2005-2014) record high or record low was broken in 2015.\n",
    "3. Watch out for leap days (i.e. February 29th), it is reasonable to remove these points from the dataset for the purpose of this visualization.\n",
    "4. Make the visual nice! Leverage principles from the first module in this course when developing your solution. Consider issues such as legends, labels, and chart junk.\n",
    "\n",
    "The data you have been given is near **Ann Arbor, Michigan, United States**, and the stations the data comes from are shown on the map below."
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if (gjData.features.length != 0) {
  var gj = L.geoJson(gjData, {
    style: function (feature) {
      return feature.properties;
    },
    pointToLayer: function (feature, latlng) {
      var icon = L.divIcon({'html': feature.properties.html, 
        iconAnchor: [feature.properties.anchor_x, 
                     feature.properties.anchor_y], 
          className: 'empty'});  // What can I do about empty?
      return L.marker(latlng, {icon: icon});
    }
  });
  gj.addTo(map);
  
  map.fitBounds(gj.getBounds());
} else {
  map.setView([0, 0], 1);
}
</script>
</body>\" width=\"100%\" height=\"480\"></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import mplleaflet\n",
    "import pandas as pd\n",
    "\n",
    "def leaflet_plot_stations(binsize, hashid):\n",
    "\n",
    "    df = pd.read_csv('data/C2A2_data/BinSize_d{}.csv'.format(binsize))\n",
    "\n",
    "    station_locations_by_hash = df[df['hash'] == hashid]\n",
    "\n",
    "    lons = station_locations_by_hash['LONGITUDE'].tolist()\n",
    "    lats = station_locations_by_hash['LATITUDE'].tolist()\n",
    "\n",
    "    plt.figure(figsize=(8,8))\n",
    "\n",
    "    plt.scatter(lons, lats, c='r', alpha=0.7, s=200)\n",
    "\n",
    "    return mplleaflet.display()\n",
    "\n",
    "leaflet_plot_stations(400,'fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jxwOYTvBZs2bZCJANGzaw+vhxbtcf+OVAkIMDVzk7Yy8Eb775Jk5OTjaVYyMj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f027e9eb240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %matplotlib notebook\n",
    "import numpy as np\n",
    "\n",
    "df = pd.read_csv('data/C2A2_data/BinnedCsvs_d400/fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89.csv')\n",
    "\n",
    "#Split year and month-day\n",
    "df['Year'], df['Month-Day'] = zip(*df['Date'].apply(lambda x : (x[:4], x[5:])))\n",
    "\n",
    "#Alternatives:\n",
    "# df['Year'] = df['Date'].apply(lambda x : x[:4])\n",
    "# df['Month-Day'] = df['Date'].apply(lambda x : x[5:])\n",
    "\n",
    "#Get the dataframe without the date of 02-29\n",
    "df = df[df['Month-Day'] != '02-29']\n",
    "\n",
    "#Get the min and max value for years from 2005 to 2014\n",
    "df_min = df[(df['Year'] != '2015') & (df['Element'] == 'TMIN')].groupby('Month-Day')['Data_Value'].agg({'MIN' : np.min})\n",
    "df_max = df[(df['Year'] != '2015') & (df['Element'] == 'TMAX')].groupby('Month-Day')['Data_Value'].agg({'MAX' : np.max})\n",
    "\n",
    "#Get the min and max value for the year of 2015\n",
    "df_min_2015 = df[(df['Year'] == '2015') & (df['Element'] == 'TMIN')].groupby('Month-Day')['Data_Value'].agg({'MIN' : np.min})\n",
    "df_max_2015 = df[(df['Year'] == '2015') & (df['Element'] == 'TMAX')].groupby('Month-Day')['Data_Value'].agg({'MAX' : np.max})\n",
    "\n",
    "#[0] -> for rows, [1] -> for columns\n",
    "a = np.where(df_min['MIN'] > df_min_2015['MIN'])[0]\n",
    "b = np.where(df_max['MAX'] < df_max_2015['MAX'])[0]\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "#Scartter Plot of 2015\n",
    "plt.scatter(a, df_min_2015.iloc[a], s = 30, c = 'blue', label = 'broken low')\n",
    "plt.scatter(b, df_max_2015.iloc[b], s = 30, c = 'red', label = 'broken high')\n",
    "\n",
    "df_min = df_min.reset_index()\n",
    "df_max = df_max.reset_index()\n",
    "\n",
    "#Line Plot for years of 2005 - 2014\n",
    "plt.plot(df_min['MIN'], color = 'green', label = 'record low temperature')\n",
    "plt.plot(df_max['MAX'], color = 'black', label = 'record high temperature')\n",
    "\n",
    "\n",
    "#Shade the area between the record high and record low temperatures for each day\n",
    "plt.gca().fill_between(range(len(df_min)), df_min['MIN'], df_max['MAX'], facecolor = 'grey', alpha = 0.25)\n",
    "\n",
    "\n",
    "#Dejunking\n",
    "\n",
    "#Set title, xlabel, ylabel, legend\n",
    "plt.title('Ann Arbor Weather Plot')\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Temperature in Celsius')\n",
    "plt.legend()\n",
    "\n",
    "#Remove left spine and top spine\n",
    "ax = plt.gca()\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "plt.xticks(range(0, len(df_min), 30), df_min['Month-Day'].iloc[range(0, len(df_min), 30)], rotation = '45')\n",
    "\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
